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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2506.20899 |
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| _version_ | 1866915359603294208 |
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| author | Perera, Dilina McAllister, Samuel Cerdà, Joan Josep Vogel, Thomas |
| author_facet | Perera, Dilina McAllister, Samuel Cerdà, Joan Josep Vogel, Thomas |
| contents | We use a semi-supervised, neural-network based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chains of magnetic colloidal nanoparticles characterized by dipole-dipole and Lennard-Jones interactions. As input for the neural network we use the particle positions and magnetic dipole moments of equilibrium polymer configurations, which we generate via replica-exchange Wang--Landau simulations. We demonstrate that by measuring the classification accuracy of neural networks, we can effectively identify transition points between multiple structural phases without any prior knowledge of their existence or location. We corroborate our findings by investigating relevant, conventional order parameters. Our study furthermore examines previously unexplored low-temperature regions of the phase diagram, where we find new structural transitions between highly ordered helicoidal polymer configurations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20899 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer Perera, Dilina McAllister, Samuel Cerdà, Joan Josep Vogel, Thomas Soft Condensed Matter We use a semi-supervised, neural-network based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chains of magnetic colloidal nanoparticles characterized by dipole-dipole and Lennard-Jones interactions. As input for the neural network we use the particle positions and magnetic dipole moments of equilibrium polymer configurations, which we generate via replica-exchange Wang--Landau simulations. We demonstrate that by measuring the classification accuracy of neural networks, we can effectively identify transition points between multiple structural phases without any prior knowledge of their existence or location. We corroborate our findings by investigating relevant, conventional order parameters. Our study furthermore examines previously unexplored low-temperature regions of the phase diagram, where we find new structural transitions between highly ordered helicoidal polymer configurations. |
| title | Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2506.20899 |